---
title: "SMILE — paper by Qiu et al. (2024) — summary and ideas"
description: "The authors propose SMILE, a prompting technique in which ChatGPT (gpt-3.5-turbo-0613) turns… 39 references, cited by 49 papers here."
authors: ["Huachuan Qiu", "Hongliang He", "Shuai Zhang", "Anqi Li", "Zhenzhong Lan"]
year: 2024
venue: "Findings of the Association for Computational Linguistics: EMNLP 2024"
source: "https://aclanthology.org/2024.findings-emnlp.34/"
doi: "10.18653/v1/2024.findings-emnlp.34"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/qiu-2024-smile-single-turn-multi-turn/"
---
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# SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support

**Authors:** [Huachuan Qiu](https://md.research.elamur.ai/free/en/authors/huachuan-qiu/), [Hongliang He](https://md.research.elamur.ai/free/en/authors/hongliang-he/), [Shuai Zhang](https://md.research.elamur.ai/free/en/authors/shuai-zhang/), Anqi Li and [Zhenzhong Lan](https://md.research.elamur.ai/free/en/authors/zhenzhong-lan/) · **Year:** 2024 · **Venue:** [Findings of the Association for Computational Linguistics: EMNLP 2024](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2024](https://md.research.elamur.ai/free/en/venues/findings/2024/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.findings-emnlp.34/) · [PDF](https://aclanthology.org/2024.findings-emnlp.34.pdf) · [DOI 10.18653/v1/2024.findings-emnlp.34](https://doi.org/10.18653/v1/2024.findings-emnlp.34) · [OpenAlex](https://openalex.org/W4404783465)

**Citations:** 46 citations (OpenAlex, 2026-10-14) · [49](https://md.research.elamur.ai/free/en/papers/qiu-2024-smile-single-turn-multi-turn/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Mental health dialogue systems need multi-turn conversation data, but real counseling sessions are hard to collect and share. They raise privacy concerns, and crowdsourcing is slow and expensive. The authors propose SMILE, a prompting technique in which ChatGPT (gpt-3.5-turbo-0613) turns public single-turn question–answer pairs into multi-turn dialogues between a help-seeker and a supporter. They apply it to PsyQA, a Chinese mental-health QA corpus. Before rewriting, the data are cleaned of forum-specific wording and truncated to fit the model's context window. Analyses of language transformation and of lexical, semantic and topic diversity indicate that the rewritten dialogues are realistic and varied. The result is SMILECHAT, a corpus of about 55k Chinese dialogues, which the authors use to fine-tune a counseling chatbot, MeChat. For evaluation, they gathered PsyTest, a small set of anonymized real counseling sessions. Both automatic metrics and human judgments show clear gains over a model not trained on SMILECHAT. The approach offers a low-cost way to build multi-turn data, and the authors suggest it could also work in medical, financial or legal settings.

## Key points
- **SMILECHAT:** 55,165 Chinese multi-turn dialogues averaging 5.7 turns, derived from PsyQA. The prompt asks ChatGPT for at least 10 help-seeker/supporter exchanges.
- **Preprocessing:** PsyQA wording was cleaned automatically and manually, for example removing forum terms like "楼主". QA pairs were cut at 1800 Chinese characters, a limit that 757 QAs exceeded.
- **PsyTest:** 50 anonymized real-life counseling dialogues. Access is restricted and requires IRB approval and a nondisclosure agreement.
- **Human evaluation:** In 36% of sessions, evaluators rated MeChat's responses as somewhat better than the reference (golden) response. A case study shows that the fine-tuned model gives shorter, more supportive replies than the baseline.
- **Release and limitations:** Code, data and models are released at github.com/qiuhuachuan/smile. The authors note two limitations: automatic metrics suffer from the one-to-many problem, and the dialogues are relatively short.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Zhejiang University](https://md.research.elamur.ai/free/en/universities/zhejiang-university/) — University, Hangzhou, China · 46 works · 11,542 citations
- [Westlake University](https://md.research.elamur.ai/free/en/universities/westlake-university/) — University, Hangzhou, China · 30 works · 7,732 citations

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## Cite

Cite as: Huachuan Qiu, Hongliang He, Shuai Zhang, Anqi Li and Zhenzhong Lan. 2024. SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support. Findings of the Association for Computational Linguistics: EMNLP 2024.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{qiu2024smile,
  title = {{SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support}},
  author = {Huachuan Qiu, Hongliang He, Shuai Zhang, Anqi Li and Zhenzhong Lan},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2024},
  year = {2024},
  doi = {10.18653/v1/2024.findings-emnlp.34},
  url = {https://doi.org/10.18653/v1/2024.findings-emnlp.34}
}
```

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37. Weixiang Zhao, Yanyan Zhao, Xin Lu, Shilong Wang, Yanpeng Tong, and Bing Qin. 2023. Is chatgpt equipped with emotional dialogue capabilities? [find](https://scholar.google.com/scholar?q=Weixiang+Zhao%2C+Yanyan+Zhao%2C+Xin+Lu%2C+Shilong+Wang%2C+Yanpeng+Tong%2C+and+Bing+Qin%2E+2023%2E+Is+chatgpt+equipped+with+emotional+dialogue+capabilities%3F)
38. Chujie Zheng, Sahand Sabour, Jiaxin Wen, and Minlie Huang. 2022. Augesc: Large-scale data augmentation for emotional support conversation with pre-trained language models. arXiv preprint arXiv:2202.13047. — [arXiv:2202.13047](https://arxiv.org/abs/2202.13047) [find](https://scholar.google.com/scholar?q=Chujie+Zheng%2C+Sahand+Sabour%2C+Jiaxin+Wen%2C+and+Minlie+Huang%2E+2022%2E+Augesc%3A+Large%2Dscale+data+augmentation+for+emotional+support+conversation+with+pre%2Dtrained+language+models%2E+arXiv+preprint+arXiv%3A2202%2E13)
39. Yiming Zhu, Peixian Zhang, Ehsan-Ul Haq, Pan Hui, and Gareth Tyson. 2023. Can chatgpt reproduce human-generated labels? a study of social computing tasks. [find](https://scholar.google.com/scholar?q=Yiming+Zhu%2C+Peixian+Zhang%2C+Ehsan%2DUl+Haq%2C+Pan+Hui%2C+and+Gareth+Tyson%2E+2023%2E+Can+chatgpt+reproduce+human%2Dgenerated+labels%3F+a+study+of+social+computing+tasks%2E)

*Updated 24 October 2026*